https://popups.uliege.be/esaform21/index.php?id=1065 Publications of Auteurs Geir Ringen fr 0 https://popups.uliege.be/esaform21/index.php?id=4040 Stretch bending is commonly used in the mass production of profile-like products in many industrial sectors due to its high dimensional accuracy and process capabilities. One of the challenges of conventional stretch bending is low flexibility, however, making it difficult to meet today’s requirements for mass customization. As a countermeasure, a novel flexible rotary stretch bending process was presented (Ma and Welo, 2021), which allows the forming of complex shapes with varying curvatures and angles. However, less knowledge is known about the most fundamental force requirements during forming, which in turn limits the design and development of product and process. In this research, an analytical model is developed for accurate and efficient prediction of real-time forming forces in flexible rotary stretch bending, aiming to enhance the understanding of applied force requirements throughout the process. In this model, the entire kinematically-controlled loading (strain) history is considered to realize real-time monitoring of force. In addition, the elastic-plastic properties of profile, the profile dimensions, the tooling geometries as well as the tool-workpiece friction are comprehensively taken into account to improve the analytical accuracy of forming force predictions. As an explicit solution can be achieved, the analytical model presents high efficiency for quick prediction, which can be used in attempts to adaptively control the process. Based on finite element simulation, the analytical model is validated in the forming of aluminium rectangular, hollow profiles, showing very high accuracy and efficiency for predicting real-time forming forces of both clamp unit and bending die for forming with different pre-stretching levels. Tue, 30 Mar 2021 10:55:06 +0200 Tue, 30 Mar 2021 10:55:06 +0200 https://popups.uliege.be/esaform21/index.php?id=4040 https://popups.uliege.be/esaform21/index.php?id=4002 Bending processes have various advantages, such as less processing time, lower number of tooling parts, and cost compared to other manufacturing processes. However, one of the disadvantages of a bending process is the inevitable springback problem, which entails geometrical inaccuracy. Many researchers have made attempts to effectively measure springback in-line to control product quality and compensate for variability. While measurement tools and machines are available to measure springback, they might not be able to accommodate large products due to the size limit of measurement devices. Nevertheless, sensor-based monitoring is becoming critical to control product quality and to move towards Industry 4.0. In this paper, an in-situ springback monitoring technique for bending of large-size profiles is proposed to overcome the measurement restrictions for such profiles. A computer vision technique with the circular Hough transform was used to evaluate springback. The marked points on a profile were used to track the deformation of the workpiece. However, a weakness with image processing is to recognize the points from the complex background. Instead of employing global search for the points in an image frame, the marked points were detected by locally setting regions based on forming parameters such as a bending angle and stretching level. Springback was calculated by the change of position of those points. The results of springback monitoring were validated with the physically measured data from experiments. Based on this measurement technique, the feasibility of a computer vision-based springback monitoring in large-size profile bending is discussed in detail. Tue, 30 Mar 2021 09:56:07 +0200 Mon, 12 Apr 2021 09:47:25 +0200 https://popups.uliege.be/esaform21/index.php?id=4002